With rapid increase of vehicle sale/purchase, the automatic vehicle identification systems have become imperative for effective traffic control and security applications, such as detecting traffic violations and theft access control to restricted areas, tracking of wanted vehicles, and the like. The most common technique used by automatic vehicle identification systems is the number plate/license plate detection. In this technique, a plurality of regions of interest is identified in an image, and character segmentation is performed using feature extraction mechanisms.
The existing license plate detection techniques use gradient and edge information from one or more filters, along with a sliding window technique. An example of the one or more filters is Sobel. Additionally, a Hough transform based approach is employed. Typically, for representing characters, the existing license plate detection techniques use features such as scale-invariant feature transform (SIFT), Histogram of Gradients (HoG), or Haar-like. In some cases, the features have been supplemented with learning based methods, such as Support Vector Machine (SVM), Boosting, and the like. A major disadvantage of the existing license plate detection techniques is the complexity and the computational burden which results in inaccurate character recognition. As the number of images to be analysed increases, the mechanisms used by the existing license plate detection techniques cannot match up the desired processing speed. Another disadvantage is that the techniques rely on a single learning model. This model is not sufficient to identify license plate formats across countries, or even within states. Further, the techniques cannot accurately recognize characters in low lighting or visibility conditions. Examples include the change of light in day and night, change of weather, and the like. In addition, if the input image/video has low resolution, the character recognition becomes challenging. Therefore, there is a need for an accurate and computationally efficient solution for solving the problem of license plate identification and character recognition.